Reinforcement-based Simultaneous Algorithm and its Hyperparameters Selection

TL;DR

This paper introduces a reinforcement learning approach that simultaneously selects algorithms and hyperparameters for data analysis, framing it as a multi-armed bandit problem, and demonstrates its effectiveness over existing methods.

Contribution

The paper proposes a novel method that reduces the combined algorithm and hyperparameter selection problem to a multi-armed bandit framework, improving selection efficiency.

Findings

Our method outperforms Auto-WEKA on most datasets.

It is never worse than Auto-WEKA in the experiments.

The approach effectively balances exploration and exploitation in selection.

Abstract

Many algorithms for data analysis exist, especially for classification problems. To solve a data analysis problem, a proper algorithm should be chosen, and also its hyperparameters should be selected. In this paper, we present a new method for the simultaneous selection of an algorithm and its hyperparameters. In order to do so, we reduced this problem to the multi-armed bandit problem. We consider an algorithm as an arm and algorithm hyperparameters search during a fixed time as the corresponding arm play. We also suggest a problem-specific reward function. We performed the experiments on 10 real datasets and compare the suggested method with the existing one implemented in Auto-WEKA. The results show that our method is significantly better in most of the cases and never worse than the Auto-WEKA.